import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_summary import summary
from torch_pydot import plot_model

class BasicBlock(nn.Module):
  expansion = 1
  
  def __init__(self, in_planes, planes, stride=1):
    super(BasicBlock, self).__init__()
    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    
    self.shortcux = nn.Sequential()
    if stride != 1 or in_planes != self.expansion * planes:
      self.shortcux = nn.Sequential(
        nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(self.expansion * planes),
      )
  
  def forward(self, x):
    x1 = F.relu(self.bn1(self.conv1(x)))
    x1 = self.bn2(self.conv2(x1))
    x1 += self.shortcux(x)
    x1 = F.relu(x1)
    return x1


class RestBottleneck(nn.Module):
  expansion = 4
  
  def __init__(self, in_planes, planes, stride=1):
    super(RestBottleneck, self).__init__()
    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(self.expansion * planes)
    
    self.shortcux = nn.Sequential()
    
    if stride != 1 or in_planes != self.expansion * planes:
      self.shortcux = nn.Sequential(
        nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(self.expansion * planes),
      )
  
  def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = F.relu(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    out += self.shortcux(x)
    out = F.relu(out)

    return out


class ResNet(nn.Module):
  def __init__(self, block, num_blocks, num_classes=10):
    super(ResNet, self).__init__()
    self.in_planes = 64
    self.conv1 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
    self.bn = nn.BatchNorm2d(64)
    self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
    self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
    self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
    self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
    
    self.fc = nn.Linear(512 * block.expansion, num_classes)
  
  def _make_layer(self, block, planes, num_blocks, stride):
    strides = [stride] + [1] * (num_blocks - 1)
    layers = []
    for stride in strides:
      layers.append(block(self.in_planes, planes, stride))
      self.in_planes = planes * block.expansion
    return nn.Sequential(*layers)
  
  def forward(self, x):
    out = F.relu(self.bn(self.conv1(x)))
    out = self.layer1(out)
    out = self.layer2(out)
    out = self.layer3(out)
    out = self.layer4(out)
    out = nn.AvgPool2d(out.size(2))(out)
    out = out.view(out.size(0), -1)
    out = self.fc(out)
    
    return out


def ResNet18():
  return ResNet(BasicBlock, [2, 2, 2, 2])


def ResNet34():
  return ResNet(BasicBlock, [3, 4, 6, 3])


def ResNet50():
  return ResNet(RestBottleneck, [3, 4, 6, 3])


def ResNet101():
  return ResNet(RestBottleneck, [3, 4, 23, 3])

net=ResNet50()

summary(net,input_size=(3,32,32))
plot_model(net,input_size=(1,3,32,32),show_shapes=True)

x=torch.randn(6,3,32,32)
print(net(x).size())
